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Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed
journal contributionposted on 2017-08-22, 12:38 authored by Luoke Hu, Ying Liu, Niels LohseNiels Lohse, Renzhong Tang, Jingxiang Lv, Chen Peng, Steve Evans
A considerable amount of energy consumed by machine tools is attributable to non-cutting operations, including tool path, tool change, and change of spindle rotation speed. The non-cutting energy consumption of the machine tool (NCE) is affected by the processing sequence of the features of a specific part (PFS), because the plans of non-cutting operations will vary based on the different PFS. This article aims to understand the NCE between processing a specific feature and its pre or post feature, especially the energy consumed during the speed change of the spindle rotation. Based on the developed model, a single objective optimisation problem is introduced that minimises the NCE. Then, Ant Colony Optimisation (ACO) is employed to search for the optimal PFS. A case study is developed to validate the effectiveness of the proposed approach. Two parts with 12 and 15 features are processed on a machining centre. The simulation experiment results show that the optimal or nearoptimal PFS can be found. Consequently, 8.70% and 30.42% reductions in NCE are achieved for part A and part B, respectively. Further, the performance of ACO for our specific optimisation problem is discussed and validated based on comparisons with other algorithms.
The authors would like to thank the support from the National Natural Science Foundation of China (Grant No. U1501248), the China Scholarship Council (Grant No. 01406320033), and the EPSRC Centre for Innovative Manufacturing in Intelligent Automation (Grant No. EP/I033467/1).
- Mechanical, Electrical and Manufacturing Engineering
CitationHU, L. ...et al., 2017. Sequencing the features to minimise the non-cutting energy consumption in machining considering the change of spindle rotation speed. Energy, 139, pp. 935-946.
Publisher© 2017 The Authors. Published by Elsevier Ltd.
- VoR (Version of Record)
Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/
NotesThis is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/ Supplementary data for this article is available in the Loughborough Data Repository at doi: 10.17028/rd.lboro.5570041